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dengwenjie
2025-08-29 10:30:35 +08:00
parent 8bf620a330
commit 86ea7eb03e
364 changed files with 8572 additions and 540 deletions
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10
examples/face-example/pom.xml
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@@ -12,7 +12,7 @@
<maven.compiler.source>11</maven.compiler.source>
<maven.compiler.target>11</maven.compiler.target>
<project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
<smartjavaai.version>1.0.23</smartjavaai.version>
<smartjavaai.version>1.0.24</smartjavaai.version>
<!--如果打包运行，需要替换成你的main-->
<exec.mainClass>smartai.examples.face.facedet.FaceDetDemo</exec.mainClass>
@@ -255,6 +255,14 @@
<scope>runtime</scope>
</dependency>
<dependency>
<groupId>ai.djl.pytorch</groupId>
<artifactId>pytorch-native-cpu</artifactId>
<classifier>linux-aarch64</classifier>
<scope>runtime</scope>
<version>2.5.1</version>
</dependency>
</dependencies>

118
examples/face-example/src/main/java/smartai/examples/face/PythonTranslator.java Normal file
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@@ -0,0 +1,118 @@
/*
* Copyright 2023 Amazon.com, Inc. or its affiliates. All Rights Reserved.
*
* Licensed under the Apache License, Version 2.0 (the "License"). You may not use this file except in compliance
* with the License. A copy of the License is located at
*
* http://aws.amazon.com/apache2.0/
*
* or in the "license" file accompanying this file. This file is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES
* OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions
* and limitations under the License.
*/
package smartai.examples.face;
import ai.djl.ModelException;
import ai.djl.inference.Predictor;
import ai.djl.modality.Classifications;
import ai.djl.modality.Input;
import ai.djl.modality.Output;
import ai.djl.modality.cv.Image;
import ai.djl.ndarray.NDList;
import ai.djl.repository.zoo.Criteria;
import ai.djl.repository.zoo.ZooModel;
import ai.djl.translate.NoBatchifyTranslator;
import ai.djl.translate.TranslateException;
import ai.djl.translate.TranslatorContext;
import ai.djl.util.JsonUtils;
import ai.djl.util.Utils;
import com.google.gson.reflect.TypeToken;
import java.io.IOException;
import java.io.InputStream;
import java.lang.reflect.Type;
import java.net.URL;
import java.nio.file.Paths;
import java.util.ArrayList;
import java.util.List;
import java.util.Map;
public class PythonTranslator implements NoBatchifyTranslator<byte[], Classifications> {
private ZooModel<Input, Output> model;
private Predictor<Input, Output> predictor;
@Override
public void prepare(TranslatorContext ctx) throws ModelException, IOException {
if (predictor == null) {
Criteria<Input, Output> criteria =
Criteria.builder()
.setTypes(Input.class, Output.class)
.optModelPath(Paths.get("src/test/python"))
.optEngine("Python")
.build();
model = criteria.loadModel();
predictor = model.newPredictor();
}
}
// @Override
// public NDList processInput(TranslatorContext ctx, String url)
// throws IOException, TranslateException {
// Input input = new Input();
// try (InputStream is = new URL(url).openStream()) {
// input.add("data", Utils.toByteArray(is));
// }
// input.addProperty("Content-Type", "image/jpeg");
// // calling preprocess() function in model.py
// input.addProperty("handler", "preprocess");
// Output output = predictor.predict(input);
// if (output.getCode() != 200) {
// throw new TranslateException("Python preprocess() failed: " + output.getMessage());
// }
//
// return output.getDataAsNDList(ctx.getNDManager());
// }
@Override
public NDList processInput(TranslatorContext ctx, byte[] image)
throws IOException, TranslateException {
Input input = new Input();
input.add("data", image);
input.addProperty("Content-Type", "image/jpeg");
// calling preprocess() function in model.py
input.addProperty("handler", "preprocess");
Output output = predictor.predict(input);
if (output.getCode() != 200) {
throw new TranslateException("Python preprocess() failed: " + output.getMessage());
}
return output.getDataAsNDList(ctx.getNDManager());
}
@Override
public Classifications processOutput(TranslatorContext ctx, NDList list)
throws TranslateException {
Input input = new Input();
input.add("data", list);
// calling postprocess() function in processing.py
input.addProperty("handler", "postprocess");
Output output = predictor.predict(input);
if (output.getCode() != 200) {
throw new TranslateException("Python postprocess() failed: " + output.getMessage());
}
String json = output.getData().getAsString();
System.out.println("json:" + json);
return null;
}
public void close() {
if (predictor != null) {
predictor.close();
model.close();
predictor = null;
model = null;
}
}
}

99
examples/face-example/src/main/java/smartai/examples/face/Test.java Normal file
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@@ -0,0 +1,99 @@
package smartai.examples.face;
import ai.djl.Application;
import ai.djl.Device;
import ai.djl.MalformedModelException;
import ai.djl.inference.Predictor;
import ai.djl.modality.Classifications;
import ai.djl.modality.audio.Audio;
import ai.djl.modality.audio.AudioFactory;
import ai.djl.modality.audio.translator.SpeechRecognitionTranslatorFactory;
import ai.djl.repository.Artifact;
import ai.djl.repository.MRL;
import ai.djl.repository.zoo.Criteria;
import ai.djl.repository.zoo.ModelNotFoundException;
import ai.djl.repository.zoo.ModelZoo;
import ai.djl.repository.zoo.ZooModel;
import ai.djl.translate.TranslateException;
import lombok.extern.slf4j.Slf4j;
import java.io.IOException;
import java.nio.file.Files;
import java.nio.file.Path;
import java.nio.file.Paths;
import java.util.List;
import java.util.Map;
/**
* @author dwj
* @date 2025/7/29
*/
@Slf4j
public class Test {
public static void main(String[] args) throws ModelNotFoundException, MalformedModelException, IOException, TranslateException {
// PythonTranslator translator = new PythonTranslator();
// Criteria<byte[], Classifications> criteria =
// Criteria.builder()
// .setTypes(byte[].class, Classifications.class)
// .optModelPath(Paths.get("/Users/wenjie/Documents/develop/model/arcfaceresnet100-11-int8.onnx"))
// .optEngine("OnnxRuntime")
// .optTranslator(translator)
// .build();
// String path = "/Users/wenjie/Downloads/facetest/jsy.jpg";
// try (ZooModel<byte[], Classifications> model = criteria.loadModel();
// Predictor<byte[], Classifications> predictor = model.newPredictor()) {
// byte[] data = Files.readAllBytes(Paths.get(path));
// Classifications ret = predictor.predict(data);
// System.out.println(ret);
// }
//
// // unload python model
// translator.close();
// Load model.
// Wav2Vec2 model is a speech model that accepts a float array corresponding to the raw
// waveform of the speech signal.
// String url = "/Users/wenjie/Downloads/20210601_u2++_conformer_exp/final.pt";
// Criteria<Audio, String> criteria =
// Criteria.builder()
// .setTypes(Audio.class, String.class)
//// .optModelUrls(url)
// .optModelPath(Paths.get(url))
// .optDevice(Device.cpu()) // torchscript model only support CPU
// .optTranslatorFactory(new SpeechRecognitionTranslatorFactory())
//// .optModelName("data.pkl")
// .optEngine("PyTorch")
// .build();
//
// // Read in audio file
// String wave = "https://resources.djl.ai/audios/speech.wav";
// Audio audio = AudioFactory.newInstance().fromUrl(wave);
// try (ZooModel<Audio, String> model = criteria.loadModel();
// Predictor<Audio, String> predictor = model.newPredictor()) {
// String result = predictor.predict(audio);
// log.info("Result: {}", result);
// }
boolean withArtifacts =
args.length > 0 && ("--artifact".equals(args[0]) || "-a".equals(args[0]));
if (!withArtifacts) {
log.info("============================================================");
log.info("user ./gradlew listModel --args='-a' to show artifact detail");
log.info("============================================================");
}
Map<Application, List<Artifact>> models = ModelZoo.listModels();
for (Map.Entry<Application, List<Artifact>> entry : models.entrySet()) {
String appName = entry.getKey().toString();
for (Artifact artifact : entry.getValue()) {
if (withArtifacts) {
log.info("{} djl://{}", appName, artifact);
} else {
log.info("{} {}", appName, artifact);
}
}
}
}
}

19
examples/face-example/src/main/java/smartai/examples/face/facedet/FaceDetDemo.java
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@@ -67,7 +67,7 @@ public class FaceDetDemo {
//高精度模型，速度慢
config.setModelEnum(FaceDetModelEnum.RETINA_FACE);//人脸检测模型
//下载模型并替换本地路径，下载地址：https://pan.baidu.com/s/10l22x5fRz_gwLr8EAHa1Jg?pwd=1234 提取码: 1234
config.setModelPath("/Users/xxx/Documents/develop/model/retinaface.pt");
config.setModelPath("/Users/wenjie/Documents/develop/face_model/retinaface.pt");
config.setConfidenceThreshold(FaceDetectConstant.DEFAULT_CONFIDENCE_THRESHOLD);//只返回相似度大于该值的人脸
config.setNmsThresh(FaceDetectConstant.NMS_THRESHOLD);//用于去除重复的人脸框，当两个框的重叠度超过该值时，只保留一个
return FaceDetModelFactory.getInstance().getModel(config);
@@ -95,12 +95,21 @@ public class FaceDetDemo {
@Test
public void testFaceDetect(){
try {
FaceDetModel faceModel = FaceDetModelFactory.getInstance().getModel();
FaceDetModel faceModel = getFaceDetModel();
R<DetectionResponse> detectedResult = faceModel.detect(imgPath);
if(detectedResult.isSuccess()){
log.info("人脸检测结果：{}", JSONObject.toJSONString(detectedResult.getData()));
// if(detectedResult.isSuccess()){
// log.info("人脸检测结果：{}", JSONObject.toJSONString(detectedResult.getData()));
// }else{
// log.info("人脸检测失败：{}", detectedResult.getMessage());
// }
long start = System.currentTimeMillis();
R<DetectionResponse> detectedResult2 = faceModel.detect("/Users/wenjie/Downloads/facetest/surprise.png");
log.info("耗时：{}", System.currentTimeMillis() - start);
if(detectedResult2.isSuccess()){
log.info("人脸检测结果：{}", JSONObject.toJSONString(detectedResult2.getData()));
}else{
log.info("人脸检测失败：{}", detectedResult.getMessage());
log.info("人脸检测失败：{}", detectedResult2.getMessage());
}
} catch (Exception e) {
e.printStackTrace();

4
examples/face-example/src/main/java/smartai/examples/face/facerec/FaceRecDemo.java
View File

@@ -63,7 +63,7 @@ public class FaceRecDemo {
//高精度模型，速度慢
config.setModelEnum(FaceDetModelEnum.RETINA_FACE);//人脸检测模型
//下载模型并替换本地路径，下载地址：https://pan.baidu.com/s/10l22x5fRz_gwLr8EAHa1Jg?pwd=1234 提取码: 1234
config.setModelPath("/Users/xxx/Documents/develop/model/retinaface.pt");
// config.setModelPath("/Users/wenjie/Documents/develop/model/retinaface.pt");
config.setConfidenceThreshold(FaceDetectConstant.DEFAULT_CONFIDENCE_THRESHOLD);//只返回相似度大于该值的人脸
config.setNmsThresh(FaceDetectConstant.NMS_THRESHOLD);//用于去除重复的人脸框，当两个框的重叠度超过该值时，只保留一个
config.setDevice(device);
@@ -170,7 +170,7 @@ public class FaceRecDemo {
FaceRecConfig config = new FaceRecConfig();
//高精度模型，速度慢, 追求速度请更换高速模型，具体其他模型参数可以查看文档：http://doc.smartjavaai.cn/face.html
config.setModelEnum(FaceRecModelEnum.ELASTIC_FACE_MODEL);//人脸检测模型
config.setModelPath("/Users/xxx/Documents/develop/model/elasticface.pt");
config.setModelPath("/Users/wenjie/Documents/develop/model/elasticface.pt");
//裁剪人脸：如果图片已经是裁剪过的，则请将此参数设置为false
config.setCropFace(true);
//开启人脸对齐：适用于人脸不正的场景，开启将提升人脸特征准确度，关闭可以提升性能

126
examples/face-example/src/test/python/model.py Normal file
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@@ -0,0 +1,126 @@
#!/usr/bin/env python
#
# Copyright 2023 Amazon.com, Inc. or its affiliates. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License"). You may not use this file
# except in compliance with the License. A copy of the License is located at
#
# http://aws.amazon.com/apache2.0/
#
# or in the "LICENSE.txt" file accompanying this file. This file is distributed on an "AS IS"
# BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, express or implied. See the License for
# the specific language governing permissions and limitations under the License.
"""
PyTorch resnet18 pre/post processing example.
"""
import json
import logging
import os
from typing import Optional, Any
import sklearn
import torch
import torch.nn.functional as F
from torchvision import transforms
from djl_python import Input
from djl_python import Output
class Processing(object):
def __init__(self):
self.topK = 5
self.image_processing = None
self.mapping = None
self.initialized = False
def initialize(self, properties: dict):
"""
Initialize model.
"""
self.image_processing = transforms.Compose([
transforms.Resize(112),
transforms.CenterCrop(112),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
])
#self.mapping = self.load_label_mapping("index_to_name.json")
self.initialized = True
def preprocess(self, inputs: Input) -> Output:
outputs = Output()
try:
batch = inputs.get_batches()
images = []
for i, item in enumerate(batch):
image = self.image_processing(item.get_as_image())
images.append(image)
images = torch.stack(images)
outputs.add_as_numpy(images.detach().numpy())
outputs.add_property("content-type", "tensor/ndlist")
except Exception as e:
logging.exception("pre-process failed")
# error handling
outputs = Output().error(str(e))
return outputs
def postprocess(self, inputs: Input) -> Output:
outputs = Output()
try:
data = inputs.get_as_numpy(0)[0]
item = torch.from_numpy(data)
print("data shape:", item.shape)
embedding = sklearn.preprocessing.normalize(item).flatten()
outputs.add(embedding)
except Exception as e:
logging.exception("post-process failed")
# error handling
outputs = Output().error(str(e))
return outputs
@staticmethod
def load_label_mapping(mapping_file_path: Any) -> dict:
if not os.path.isfile(mapping_file_path):
raise Exception('mapping file not found: ' + mapping_file_path)
with open(mapping_file_path) as f:
mapping = json.load(f)
if not isinstance(mapping, dict):
raise Exception('mapping file should be in "class":"label" format')
for key, value in mapping.items():
new_value = value
if isinstance(new_value, list):
new_value = value[-1]
if not isinstance(new_value, str):
raise Exception(
'labels in mapping must be either str or [str]')
mapping[key] = new_value
return mapping
_service = Processing()
def preprocess(inputs: Input) -> Output:
return _service.preprocess(inputs)
def postprocess(inputs: Input) -> Output:
return _service.postprocess(inputs)
def handle(inputs: Input) -> Optional[Output]:
"""
Default handler function
"""
if not _service.initialized:
# stateful model
_service.initialize(inputs.get_properties())
return None